Impact of Planning Decision Support Tools on Mining Operations Profitability
Upcoming SlideShare
Loading in...5
×
 

Impact of Planning Decision Support Tools on Mining Operations Profitability

on

  • 520 views

Changes in the mining industry business environment are leading to gradual changes in how the supply chain (from ore extraction at the mine to delivery at customer sites) is managed. Global demand is ...

Changes in the mining industry business environment are leading to gradual changes in how the supply chain (from ore extraction at the mine to delivery at customer sites) is managed. Global demand is flattening and available supply is increasing. This means that complex planning business models that were developed in an era of supply “push” need to be altered to accommodate a market reality of demand driven “pull”. This white paper introduces a decision support methodology that results in reduced cost, improved throughput, enhanced quality, and increased profit.

Statistics

Views

Total Views
520
Views on SlideShare
516
Embed Views
4

Actions

Likes
0
Downloads
10
Comments
0

1 Embed 4

https://twitter.com 4

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Impact of Planning Decision Support Tools on Mining Operations Profitability Impact of Planning Decision Support Tools on Mining Operations Profitability Document Transcript

  • Executive summary Changes in the mining industry business environment are leading to gradual changes in how the supply chain (from ore extraction at the mine to delivery at customer sites) is managed. Global demand is flattening and available supply is increasing. This means that complex planning business models that were developed in an era of supply “push” need to be altered to accommodate a market reality of demand driven “pull”. This white paper introduces a decision support methodology that results in reduced cost, improved throughput, enhanced quality, and increased profit. by Daniel Spitty and James Balzary 998-2095-04-08-14AR0
  • Impact of Planning Decision Support Tools on Mining Operations Profitability Schneider Electric White Paper Revision 0 Page 2 Mining companies utilize many disparate systems and repositories to help facilitate and simplify their planning and scheduling activities. Decision support systems exist all along the extraction to delivery “value chain” cycle. Oftentimes these systems operate as silos and lack of integration makes it difficult to consolidate information. As a result, when one process in the early extraction stage affects a process further down the line, inefficiencies can occur that result in higher costs and poor resource optimization (see Figure 1) This “variability” is characteristic of the complex and dynamic business environment which drives ore extraction and delivery activity. However variability within mining operations can be much more tightly controlled through the use of new technologies and methodologies which have recently been introduced to the marketplace. Over the last five years, throughput in the supply chain has been the key performance indicator (KPI) for many mining operations. This phenomenon occurred as a result of an environment where demand was high and supply was the bottleneck. Now the driving KPIs of most successful mining operations have evolved to include cost, revenue and profitability. Therefore the philosophy has changed from one of “produce at any cost” to one of “only produce if profitable”. In their 2013 review of the top 40 worldwide mining enterprises PriceWaterhouseCoopers consultants state the following: “In reaction to shareholder demands and both commodity price and cost pressures, miners have started to shift their focus. The days of maximising value by solely increasing production volumes are gone. The future is about managing productivity and improving efficiencies, both of which have suffered in recent years.” 1 Some mining enterprises have been slow to embrace these new changes. They continue to prioritize throughput as the most important objective despite the fact that market conditions are shifting. On the fiscal side, these companies are now reporting lower throughput and high maintenance activity. This paper illustrates how new methodologies and tools can enable mining operations to better manage variability in a business environment that requires dynamically updated information for faster and more accurate decision making. 1 PriceWaterhouseCoopers, “Mine: A Confidence Crisis”, Review of global trends in the mining industry—201,3 p. 5, 2013 Introduction Total time available (8760 hours) Scheduled time (loading %) Loss Available time Loss Scheduled non-operating time (hoidays etc.) 365 days x 24 hours Non-available time (downtime) Operating time Loss Non-operation time within available time (training, etc. ) Effective operating time Loss Rate losses due to operational and maintenance issues Production time Loss Quality losses (e.g. ineffective blasting) Figure 1 Summary taken from mining industry production time case studies (courtesy of PriceWaterhouseCoopers)
  • Impact of Planning Decision Support Tools on Mining Operations Profitability Schneider Electric White Paper Revision 0 Page 3 Variability exists in all areas of the mining resource-to-market supply chain. It is impacted by the material attributes of the mine and the timing in which extracted material is being provided to downstream stakeholders. Often equipment is available for use, yet it still performs below its capability. This is an example of activity that can be captured as a variance. For example, a train load car of ore cannot be used until the train arrives and is ready for loading. Therefore, even though a train car is available, another related activity such as the unavailability of loading equipment can prevent the train car from being loaded. This particular variability in performance is due to inefficient coordination issues. In an integrated supply chain environment characterized by multiple supply sources, complex processing and transport functions, and multiple loading and distribution points, the number of decision possibilities is high. This makes it difficult for planners to understand what the best decision is for the most immediate need. Defining, capturing, recording, and highlighting where the majority of these variability instances exist is difficult given the amount of dependencies and relationships within the resource-to-market mining operation. The demand profile (i.e., spot transactions vs. fulfillment of long term supply contracts) of the commodity extracted from the mine also influences the degree of variability the operation must manage. Global events such as political change and weather patterns are also factors. Most of today‟s planning systems are ill equipped to manage this degree of variability in any coordinated way. Planning personnel tend to use separate planning systems for each particular function and planning horizon. These individual systems allow the planners to utilize one set of assumptions and parameters that produce only a single plan. In addition, traditional technologies capable of modeling complex geospatial, quality and quantity variables used by geologists and mining engineers do not have the functionality to manage downstream supply chain activities. In addition, traditional supply chain planning and scheduling tools have no capability to manage niche mining operations processes. This results in “big picture” vagueness which forces planners to become more conservative in their planning. The planners respond to this environment by building in “buffer” which is hidden in their projections. An analogy which helps to illustrate this problem is a common phenomenon we all experience when planning our air travel. Often people will over compensate the time required to catch a connecting flight at an international airport. They factor in the variability of the activities such as the likelihood of the incoming flight arriving on time, the customs and security clearances, the time between gates and terminals, as well as historical airline performance and weather. The time allocated for transporting themselves from point A to point B is higher than the actual time needed due to the consequences of missing the “deadline”, or in this case, the connecting flight. As a result, the duration of the entire trip is longer than it should be. This same concept is being used to add buffer to the planning of activities in all parts of the mining life cycle. Lack of actionable information creates buffer time in the process. Planners are often focused on the immediate need within their supply chain function (such as a mining engineer producing a mine plan, or a logistics planner loading trains). Because of this they can lead themselves to utilise hard constraints on decision criteria that may not require such rigidity. This focus on the immediate need can cause future problems which are not visible to the planner at the time they make their decisions. Thus the planners become reactive problem solvers as opposed to proactive problem preventers. Managing variability “Most of today’s planning systems are ill equipped to manage this degree of variability in any coordinated way”.
  • Impact of Planning Decision Support Tools on Mining Operations Profitability Schneider Electric White Paper Revision 0 Page 4 Technology now exists that standardizes the approach to planning and scheduling across the mining operations supply chain functions and time horizons (see Figure 2). These new tools still allow the uniqueness of each function to be accurately modeled and used as the basis for optimization. Consider the example of ROM (Run of Mine) stockpiles. Often companies run a production push operation to the ROM stockpiles. From the shipping back upstream to the ROM stockpiles, it is often a pull mechanism that focuses on throughput and fulfillment of the right product to the right ship at the right time. With an integrated planning across the supply chain, decisions made at a mine planning stage can now be tracked all the way through the supply chain to determine the impact on the fulfillment of shipments or demand. This had previously been difficult, time consuming, and in some cases not possible in the time frame required. Consider that a shipment impacted by the change in the mine extraction sequence may be 10 days away. However the decision of what block to extract has to occur in the next 24 hours. These planning and scheduling horizons are managed by two separate teams with limited common responsibility regarding KPIs. Such a scenario often results in the wrong product arriving at the right place at the wrong time. This can now be overcome given that the planning teams, even if they remain separate, are referencing and viewing the same information in an integrated, one application representation of the entire supply/demand chain environment. The outcome is one version of the truth for decision support. As soon as a variable changes in the model, the downstream effects are mirrored in the system. Updates are automatically provided on what activities need to be changed by the planners. These tools also enable new scenarios to be created and updated within a matter of minutes. Then the teams can collaborate and analyze multiple scenarios to determine the best decision(s) to make in each area of the supply chain. Such an integrated decision support system facilitates the ability to embrace variability management due to the Enablers to embracing variability Figure 2 An integrated model enables planners to manage a fluid environment where short term decisions impact long term profitability
  • Impact of Planning Decision Support Tools on Mining Operations Profitability Schneider Electric White Paper Revision 0 Page 5 improvement in visibility, quicker reaction to environmental changes, and to timings of shipments at port. Integrated models provide the basis upon which optimization algorithms can assist planners to make the best decision. The model maps the complexity of the supply chain over multiple planning horizons (see Figure 3). This provides visibility to the impact of certain decisions on other processes. These decision support tools both aid the planner and place into context a methodology for achieving the overall KPIs of the business. Most mining supply chain decisions are naturally non-linear in nature. Therefore, applying linear techniques to solve these problems is a questionable approach. Asset capacities such as truck tonne kilometers (tKm), crusher throughput, and material process plant residence times are rarely linear or discrete in nature. If discrete or average inputs are used as foundation assumptions for a model, then the potential for outputs that are below expectations is high. The non-linear optimization approach designed into these tools can explore more accurate and representative possibilities than any person can perform on their own. These non-linear, techniques can provide counter intuitive solutions that break new ground and allow planners to challenge the status quo. Answers derived from algrotihms can allow planners to challenge the natural and inherent buffer that exists in their current planning assumptions. Inputs such as availability, performance, and capacities of equipment can now be closely analyzed. These variables can be applied to a time based, forward-looking calendar. In the cases where the optimizer provides a solution that a planner may not agree with, the planners can override the system and lock activities in place. Manual decisions, often a critical requirement in decision support environments need to be honoured and prioritized. However, an optimisation process that occurs following a manual decision event requires the decision to be treated as a constraint, with optimisation only occuring around the manually derived output. Figure 3 An example of the integration of the planning horizons for a mining enterprise
  • Impact of Planning Decision Support Tools on Mining Operations Profitability Schneider Electric White Paper Revision 0 Page 6 Consider an example where an integrated plan already exists and is being executed. Within this current „live‟ plan there are values assigned to both quantity and quality of a given block of material to be mined (see Figure 4). This quality and quantity of material is then used in the planning of downstream activities. In this case, the activities include a storing at a mine stockpile, process plant feed, train load out, rail service, port in loading, storing at a port stockpile and ship loading. Once the ore is processed and depositied on the finished product stockpile, the quality is sampled at the train load out which results in a better estimate of the target quality attribute (ash in a coal operation could be an example). If the ash level is higher than what was expected, this more detailed data point is imported into the integrated planning system. The planner can automatically determine if the existing planned activities for the rail service are still going to achieve the desired specification outcome. The desired outcome is that the shipment will still be within the tolerance range for ash when loaded. This potential quality constraint or violation allows the planner to see if any planned activities downstream need to change. He can determine what impact the changes to this activity can have on related activities such as stockpile capacity limitations, vessel nomination specification, or train unload time. If the actual quality result forces the consignment to be sent to another stockpile, is the equipment at the port available to perform this task? If the shipment now needs to source from an alternative stockpile, is the required stock available in an accessible port area at the required quality with available equipment? And what impact will this have on the ships that had that stock pegged to its shipment? Will these shipments now be out of specification tolerance as a result? Planning and scheduling decision support technology can enable planners to embrace variability, and to understand the impact that new, dynamic information has on the current plan. Updates can be executed based on the impact of the change while utilizing available resources in an efficient manner. Figure 4 Example of mine material block data and material quality attributes
  • Impact of Planning Decision Support Tools on Mining Operations Profitability Schneider Electric White Paper Revision 0 Page 7 Variability will forever exist for mining companies in the resource-to-market supply chain. Rather than struggling to remove the variability, mining companies should be looking to embrace it, understand it and account for it through better modeling and forward looking decision making. Mining environments are becoming increasingly dynamic. The amount of reaction time that is available for planning teams is decreasing. Credible science is needed to generate the necessary scenarios required to supplement the decisions made by planners. New tools and methodologies are available today that help to optimize mining operations across all the functional areas so that throughput, quality, and profit can be improved. Daniel Spitty is the Global Capability Development Manager for Schneider Electric's Supply and Demand Optimization Activity. He holds a Bachelor of Commerce from the University of Melbourne, Australia and joined SolveIT Software in 2010 from KPMG. Mr. Spitty moved from Australia to Toronto following Schneider Electric's acquisition of SolveIT Software in 2012. He is responsible for driving growth of the StruxureWare Supply Chain Operation suite of software (previously SolveIT Software) in North America. Daniel led the SolveIT team to implement an Integrated Planning and Optimization Solution (IPOS) in Queensland. He has been active in the strategic roadmap of IPOS and involved in projects for companies such as for BHP Billiton Mitsubishi Alliance (BMA) Coal, BHP Billiton Iron Ore, Rio Tinto Iron Ore, Fortescue Metals Group, Xstrata Coal, Xstrata Copper and Roy Hill Iron Ore. James Balzary is a qualified Geologist and Software Specialist with an extensive background in operations management improvement in the mining and resources sector. He has over 19 years of operational experience in multi-commodity open pit and underground operations, and enterprise software organisations. He holds a Bachelor of Science with Honours from James Cook University and has published technical papers to globally leading scientific publications. ©2014SchneiderElectric.Allrightsreserved. Conclusion About the authors